Manual, risky deployments
Releases depend on a few people following long checklists, often late at night.
02 / Automation
DevOps and Infrastructure Automation
Software, data pipelines and AI models all need to reach production reliably and repeatedly. DevOps and infrastructure automation replaces manual deployments and hand-built servers with pipelines, code and monitoring that make releases routine.
At a glance
- Service
- DevOps and Infrastructure Automation
- Pillar
- Automation
- Typical starting point
- Delivery pipeline and infrastructure review
- Typical first phase
- 2 to 3 weeks for review; first pipelines in 4 to 8 weeks
- Key technologies
- Cloud AI, MLOps, Data Engineering
Durations depend on scope, data readiness and integration needs.
What is DevOps Automation?
Short answer
DevOps and infrastructure automation is the practice of automating software build, testing, security checks, deployment and infrastructure provisioning using continuous integration and delivery (CI/CD) pipelines, infrastructure as code and automated monitoring, so that changes can be released frequently, safely and consistently.
Many organisations still deploy by hand: someone copies files to a server, runs scripts and hopes nothing was missed. Environments drift apart, releases happen rarely because they are risky, and rollback is improvised.
Automation changes that. Code changes trigger builds and tests automatically. Infrastructure is described in version-controlled files and created the same way every time. Deployments follow a defined pipeline with approvals where needed, and monitoring shows immediately if something has gone wrong.
For AI teams, the same principles extend to model and data pipelines, which is where DevOps meets MLOps.
Business problems
What problems does DevOps Automation solve?
These are the situations where organisations most often find this service worthwhile.
Inconsistent environments
Code works in testing but fails in production because servers were configured differently.
Slow recovery from failures
Rebuilding a server or rolling back a release takes hours or days.
Rising and unclear cloud costs
Resources are created by hand, forgotten and never cleaned up.
Security checks happen too late
Vulnerabilities are found after release, if they are found at all.
AI models stuck in notebooks
Data science work has no reliable path into production systems.
Use cases
Where can DevOps Automation be used?
Representative applications. The right use case depends on your processes, data and constraints.
| Area | Example application |
|---|---|
| Application delivery | CI/CD pipelines for web, mobile and backend services with automated tests and staged releases. |
| Cloud infrastructure | Terraform or similar code for networks, compute, databases and access policies. |
| Containers | Container builds, Kubernetes deployments, scaling rules and service configuration. |
| Security | Dependency scanning, secret detection, image scanning and policy checks in pipelines. |
| Data and ML | Automated deployment of data pipelines and model serving endpoints. |
| Operations | Automated backups, patching, certificate renewal and incident runbooks. |
Who should consider it
- Software teams releasing infrequently because deployments are painful
- Organisations moving to cloud or containers
- AI and data teams that need a path from experiment to production
- IT leaders concerned about cloud cost, security and recovery
How AARHIT approaches it
How we deliver DevOps Automation
Each stage ends with something you can review, so decisions are made on evidence rather than on promises.
Stage 1 of 6
Delivery and infrastructure review
We review repositories, build processes, environments, release practices, monitoring and cloud accounts.
Output: current-state assessment and prioritised improvement plan.
Stage 2 of 6
Target platform design
We design pipelines, environment strategy, infrastructure code structure, secrets management and access controls.
Output: target architecture and standards.
Stage 3 of 6
Pipeline implementation
CI/CD pipelines are built for priority applications with automated testing, security scanning and deployment stages.
Output: working pipelines with documentation.
Stage 4 of 6
Infrastructure as code
Existing infrastructure is captured in code or rebuilt from code, with state management and review processes.
Output: version-controlled infrastructure and environment templates.
Stage 5 of 6
Observability and reliability
Logging, metrics, tracing, alerting and dashboards are set up, along with backup and recovery procedures.
Output: monitoring stack and tested recovery runbooks.
Stage 6 of 6
Enablement and handover
Your team is trained on the pipelines and practices, and ownership is transferred with clear documentation.
Output: training sessions, guides and support arrangements.
Technology
What technologies are involved?
We choose tools on fit, cost, security and your existing estate. These are typical options, not fixed commitments.
Learn more about the underlying technologies
| Layer | Typical options |
|---|---|
| CI/CD | GitHub Actions, GitLab CI, Azure DevOps, Jenkins, Argo CD |
| Infrastructure as code | Terraform, OpenTofu, Pulumi, AWS CloudFormation, Bicep |
| Containers | Docker, Kubernetes, Helm, managed Kubernetes services |
| Security | SAST and dependency scanning, container image scanning, secrets vaults |
| Observability | Prometheus, Grafana, OpenTelemetry, cloud-native monitoring |
| Cloud | AWS, Microsoft Azure, Google Cloud and Indian cloud regions |
Outcomes
Potential business outcomes
Targets are agreed with you during scoping and measured against a baseline. We do not quote improvement figures before we have seen your process and data.
- More frequent releases with fewer failed deployments
- Identical, reproducible environments
- Faster recovery from incidents and failures
- Better control and visibility of cloud costs
- Security issues found before code reaches production
How long does implementation generally take?
Typical timeline
A review typically takes 2 to 3 weeks. First CI/CD pipelines for priority applications are often in place within 4 to 8 weeks. Moving a full estate to infrastructure as code and container platforms is usually a phased programme over several months.
What determines complexity?
- Number and age of applications
- Existing test coverage
- Hybrid, multi-cloud or on-premises estates
- Regulatory and change control requirements
- Team familiarity with automation tools
Implementation
Implementation considerations and risks
Most difficulties in this kind of work are predictable. Planning for them early keeps delivery on track.
Automated tests come first
A fast deployment pipeline without tests simply releases defects faster. Test coverage often needs investment alongside pipelines.
Standardise gradually
Templates and golden paths help teams adopt good practice without forcing every application to change at once.
Manage secrets properly
Credentials in code or pipeline variables are a common breach path. Vaults and short-lived credentials should be standard.
Keep change control proportionate
Regulated environments need approvals, but they can be built into pipelines rather than handled through manual tickets.
| Risk to consider | How we manage it |
|---|---|
| Pipeline becomes a single point of failure | Redundant runners, documented recovery and pipeline code under version control. |
| Misconfigured infrastructure code | Peer review, automated policy checks and plan previews before changes apply. |
| Credential leakage | Secrets scanning, vault integration and least-privilege service accounts. |
| Team dependence on external engineers | Pairing, documentation and training throughout, not only at the end. |
FAQ
Questions about DevOps Automation
What is the difference between DevOps and MLOps?
DevOps automates how software is built, tested and deployed. MLOps applies similar principles to machine learning, adding data versioning, model training pipelines, model registries and monitoring for model drift.
What is infrastructure as code?
Infrastructure as code means describing servers, networks, databases and permissions in version-controlled files that tools use to create and update infrastructure automatically, instead of configuring it by hand.
Do we need Kubernetes?
Not necessarily. Kubernetes suits organisations running many services with scaling needs. Smaller estates are often better served by managed container services or platform services with less operational overhead.
Can DevOps automation work in regulated industries?
Yes. Approvals, segregation of duties, audit logs and evidence collection can be built into pipelines, which often makes compliance easier to demonstrate than with manual processes.
Which cloud provider do you work with?
We work with AWS, Microsoft Azure and Google Cloud, including their Indian regions, as well as on-premises and hybrid environments. The recommendation depends on your existing estate and requirements.
Next step
Considering DevOps Automation for your organisation?
Share the process, product or decision you have in mind. We will ask the right questions, outline options and suggest a sensible first step.